Deep Learning with Sets and Point Clouds

Siamak Ravanbakhsh; Jeff Schneider; Barnabas Poczos

ICLR 2017workshop224 citations

Deep Learning with Sets and Point Clouds

Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos

Abstract

We introduce a simple permutation equivariant layer for deep learning with set structure. This type of layer, obtained by parameter-sharing, has a simple implementation and linear-time complexity in the size of each set. We use deep permutation-invariant networks to perform point-could classification and MNIST digit summation, where in both cases the output is invariant to permutations of the input. In a semi-supervised setting, where the goal is make predictions for each instance within a set, we demonstrate the usefulness of this type of layer in set-outlier detection as well as semi-supervised learning with clustering side-information.

Deep learningStructured predictionComputer visionSupervised LearningSemi-Supervised Learning

BibTeX

@misc{
lee2017making,
title={Making Stochastic Neural Networks from Deterministic Ones},
author={Kimin Lee and Jaehyung Kim and Song Chong and Jinwoo Shin},
year={2017},
url={https://openreview.net/forum?id=B1akgy9xx}
}